COV scan 3 populations

1 Plot the covariances

pops<-c("PWS","TB","SS")
covs<-data.frame()
Variance<-data.frame()
winsize<-c("50k","75k","100k","250k","500k", "1m")

for (w in 1: length(winsize)){
    covs<-data.frame()
    for (p in 1: length(pops)){
        #covariance output file
        cov<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/3Pops_maf05_temp_cov_matrix_",pops[p],"_",winsize[w],".csv"))
        cov<-cov[,-1]
        
        ci<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/3Pops_maf05_",pops[p],"_Cov_CIs_bootstrap5000_",winsize[w],"window.csv"))
        ci<-ci[,-1]
        
        #reshape the matrix
        mat1<-cov[1:3,]
        mat2<-cov[4:6,]
        
        covdf<-data.frame()
        k=1
        for (i in 1:nrow(mat1)){
            for (j in 1:ncol(mat1)){
                covdf[k,1]<-mat2[i,j]
                covdf[k,2]<-mat1[i,j]
                k=k+1
            }
        }
        colnames(covdf)<-c("label","value")
        covdf$value<-as.numeric(covdf$value)
        covar<-covdf[grep("cov",covdf$label),]
        vars<-covdf[grep("var",covdf$label),]
        
        #remove the redundant values
        if (pops[p]!="SS") covar<-covar[!duplicated(covar[, 2]),] 
        if (pops[p]=="SS") covar<-covar[c(1,2,4),]
        
        #assign the starting time period and covering period values
        covar$year<-c(1,2,2)
        covar$series<-c("1991","1991","1996")
        
        vars$year<-c(1,2,2)
        vars$series<-c("1991","1991","1996")
        
        #assign population name
        covar$location<-pops[p]
        vars$location<-pops[p]
        
        #attach ci info
        covar$ci_l<-c(ci[1,2], ci[1,3],ci[2,3])
        covar$ci_u<-c(ci[4,2], ci[4,3],ci[5,3])
        
        #combine in to one matrix
        covs<-rbind(covs, covar)
        Variance<-rbind(Variance, vars)
    }
    
    covs$ci_l<-as.numeric(covs$ci_l)
    covs$ci_u<-as.numeric(covs$ci_u)
    ggplot(data=covs, aes(x=year, y=value, color=location, shape=series, group=interaction(location, series)))+
        geom_point(size=3, position=position_dodge(width = 0.1,preserve ="total"))+
        #geom_errorbar(data=covs, aes(x=year, y=value, ymin=ci_l, ymax=ci_u), width=.2, size=.2, position=position_dodge(width = 0.1,preserve ="total"))+
        geom_line(data=covs, aes(x=year, y=value,color=location, group=interaction(location, series)), position=position_dodge(width = 0.1,preserve ="total"))+
        ylab("Covariance")+xlab('')+theme_classic()+
        theme(axis.text.x = element_blank(),legend.title = element_blank())+
        geom_hline(yintercept = 0,color="gray70", size=0.3)+
        geom_errorbar(aes(ymin=ci_l, ymax=ci_u), width=.2, size=.2, position=position_dodge(width = 0.1,preserve ="total"))+
        scale_shape_manual(values=c(16,17),labels=c("1991-","1996-"))+
        scale_x_continuous(breaks = c(1,2))
    ggsave(paste0("../Output/COV/3Pops_Cov_overtime_CI_",winsize[w],".window.png"),width = 4.7, height = 3, dpi=300)
}    

2 Find regions with high covariances in each population

• From Temporal Covariance analysis -output covariances for each time period

2.1 Plot the covariances across the genome

#Find the regions with a high temporal covariance 
pops<-c("PWS","TB","SS")
winsize<-c("50k","75k","100k")
evens<-paste0("chr",seq(2,26, by=2))
cov.list<-list()
k=1
for (p in 1: length(pops)){
    pop<-pops[p]
    for (i in 1: length(winsize)){
        iv<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/3pops_intervals_",winsize[i],"window.csv"), row.names = 1)
        if (p==3) {
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_2017-2006_2006-1996_3Pops_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov23)
            colnames(covs)[4]<-c("cov23")
            covs$index=1:nrow(covs)
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list[[k]]<-covs
            names(cov.list)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            
            y<-min(covs$cov23, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ymax<-max(covs$cov23, na.rm=T)
            ggplot(covs, aes(x=index, y=cov23, color=color))+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            #ggsave(paste0("../Output/COV/3Pops.",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 2.7, dpi=300) 
        }
        else {
            cov12<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov12_1996-1991_2006-1996_3Pops_",winsize[i],"window.csv"), header = F)
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_2017-2006_2006-1996_3Pops_",winsize[i],"window.csv"), header = F)
            cov13<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov13_2017-2006_1996-1991_3Pops_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov12, cov23,cov13)
            colnames(covs)[4:6]<-c("cov12","cov23","cov13")
            covs$index=1:nrow(covs)
    
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list[[k]]<-covs
            names(cov.list)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            covsm<-melt(covs[,c("index","color","cov12","cov23","cov13")], id.vars = c("index", "color"))
            ymax<-max(covsm$value, na.rm=T)
            y<-min(covsm$value, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ggplot(covsm, aes(x=index, y=value, color=color))+
                facet_wrap(~variable, nrow=3)+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            #ggsave(paste0("../Output/COV/3Pops.",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 8, dpi=300)    
        }
    }
}


#Find the regions with a high temporal covariance #2 (for larger windows)
pops<-c("PWS","TB","SS")
winsize<-c("250k","500k", "1m")
evens<-paste0("chr",seq(2,26, by=2))
cov.list2<-list()
k=1
for (p in 1: length(pops)){
    pop<-pops[p]
    for (i in 1: length(winsize)){
        iv<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/3pops_intervals_",winsize[i],"window.csv"), row.names = 1)
        if (p==3) {
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_3Pops_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov23)
            colnames(covs)[4]<-c("cov23")
            covs$index=1:nrow(covs)
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list2[[k]]<-covs
            names(cov.list2)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            
            y<-min(covs$cov23, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ymax<-max(covs$cov23, na.rm=T)
            ggplot(covs, aes(x=index, y=cov23, color=color))+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            #ggsave(paste0("../Output/COV/3Pops.",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 2.7, dpi=300) 
        }
        else {
            cov12<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov12_3Pops_",winsize[i],"window.csv"), header = F)
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_3Pops_",winsize[i],"window.csv"), header = F)
            cov13<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov13_3Pops_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov12, cov23,cov13)
            colnames(covs)[4:6]<-c("cov12","cov23","cov13")
            covs$index=1:nrow(covs)
    
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list2[[k]]<-covs
            names(cov.list2)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            covsm<-melt(covs[,c("index","color","cov12","cov23","cov13")], id.vars = c("index", "color"))
            ymax<-max(covsm$value, na.rm=T)
            y<-min(covsm$value, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ggplot(covsm, aes(x=index, y=value, color=color))+
                facet_wrap(~variable, nrow=3)+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            #ggsave(paste0("../Output/COV/3Pops.",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 8, dpi=300)    
        }
    }
}

2.2 Find the outlier regions for each time period

#find how outliers overlap between different windows
cov12<-data.frame()
cov23<-data.frame()
cov13<-data.frame()

for (i in 1:length(cov.list)){
 if (grepl("PWS",names(cov.list)[i])|grepl("TB",names(cov.list)[i])){
    covs<-cov.list[[i]]
    
    pop<-gsub("_.+",'', names(cov.list)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list)[i])
    
    covs<-covs[order(covs$cov12, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs12_top<-covs[1:n,c(1:4)]
    covs12_top<-covs12_top[order(covs12_top$chrom, covs12_top$start),]
    covs12_top$window<-win
    covs12_top$pop<-pop
    cov12<-rbind(cov12, covs12_top)
    
    covs<-covs[order(covs$cov13, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs13_top<-covs[1:n,c(1:3,6)]
    covs13_top<-covs13_top[order(covs13_top$chrom, covs13_top$start),]
    covs13_top$window<-win
    covs13_top$pop<-pop
    cov13<-rbind(cov13, covs13_top)
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:3,5)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
 }
 if (grepl("SS",names(cov.list)[i])){
    covs<-cov.list2[[i]]
    
    pop<-gsub("_.+",'', names(cov.list)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list)[i])
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:4)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
    
    }
}


write.csv(cov12, "../Output/COV/3pops_top1percent_outlier_regions.cov12_new.csv",row.names = F)
write.csv(cov23, "../Output/COV/3pops_top1percent_outlier_regions.cov23_new.csv",row.names = F)
write.csv(cov13, "../Output/COV/3pops_top1percent_outlier_regions.cov13_new.csv",row.names = F)


### 
cov12<-data.frame()
cov23<-data.frame()
cov13<-data.frame()

for (i in 1:length(cov.list2)){
 if (grepl("PWS",names(cov.list2)[i])|grepl("TB",names(cov.list2)[i])){
    covs<-cov.list2[[i]]
    
    pop<-gsub("_.+",'', names(cov.list2)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list2)[i])
    
    covs<-covs[order(covs$cov12, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs12_top<-covs[1:n,c(1:4)]
    covs12_top<-covs12_top[order(covs12_top$chrom, covs12_top$start),]
    covs12_top$window<-win
    covs12_top$pop<-pop
    cov12<-rbind(cov12, covs12_top)
    
    covs<-covs[order(covs$cov13, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs13_top<-covs[1:n,c(1:3,6)]
    covs13_top<-covs13_top[order(covs13_top$chrom, covs13_top$start),]
    covs13_top$window<-win
    covs13_top$pop<-pop
    cov13<-rbind(cov13, covs13_top)
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:3,5)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
 }
 if (grepl("SS",names(cov.list2)[i])){
    covs<-cov.list2[[i]]
    
    pop<-gsub("_.+",'', names(cov.list2)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list2)[i])
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:4)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
    
    }
}

write.csv(cov12, "../Output/COV/3pops_top1percent_outlier_regions.cov12_250k-1m.csv",row.names = F)
write.csv(cov23, "../Output/COV/3pops_top1percent_outlier_regions.cov23_250k-1m.csv",row.names = F)
write.csv(cov13, "../Output/COV/3pops_top1percent_outlier_regions.cov13_250k-1m.csv",row.names = F)

#Create plots with different colors for outliers
#for COV12 and COV13 for TB and PWS (100K)
cv<-c("cov12","cov13","cov23")
winsize<-c("50k","75k","100k")

for (i in 1:length(cv)){
    if (i==1|i==2){
        for (w in 1: length(winsize)){
            #PWS
            df1<-cov.list[[paste0("PWS_", winsize[w])]]
            df1<-df1[order(df1[,cv[i]], decreasing=T),]
            n<-ceiling(nrow(df1)*0.01) #top1% region
            df1$top1<-"N"
            df1$top1[1:n]<-"PWS"
            
            #tb
            df2<-cov.list[[paste0("TB_", winsize[w])]]
            df2<-df2[order(df2[,cv[i]], decreasing=T),]
            df2$top1<-"N"
            df2$top1[1:n]<-"TB"
            
            co<-rbind(df1, df2)
    
            co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
            co$top1<-factor(co$top1, levels=c("PWS","TB","N"))
            colnames(co)[which(colnames(co)==cv[i])]<-"cov"
    
            ymax<-max(co$cov, na.rm=T)
            ggplot(co, aes(x=start/1000000, y=cov, color=top1))+
                geom_point(size=0.5)+
                facet_wrap(~chrom, ncol=4)+
                theme_classic()+ylim(-0.1,ymax)+
                scale_color_manual(values=c("deeppink","orange" ,"#ADD8E680"), labels=c("PWS", "TB", ""))+
                ylab("Covariance")+xlab('Postion (Mb)')+
                ggtitle(paste0(winsize[w]," window ",cv[i]))+
                scale_x_continuous(labels = comma)+
                guides(color = guide_legend(override.aes = list(color=c("deeppink","orange","white")), title=element_text("Top 1%")))
   
                ggsave(paste0("../Output/COV/COVscan_3pop/3Pops.",cv[i],"_perChrom_",winsize[w], "Window_Outliers.png"), width = 10, height = 8, dpi=300)
        }
       
    }
   
    if (i==3){
        for (w in 1: length(winsize)){
        #pws
        df1<-cov.list[[paste0("PWS_", winsize[w])]]
        df1<-df1[,c("chrom","start","end","cov23")]
        df1<-df1[order(df1$cov23, decreasing=T),]
        n<-ceiling(nrow(df1)*0.01) #top1% region
        df1$top1<-"N"
        df1$top1[1:n]<-"PWS"
    
        #tb
        df2<-cov.list[[paste0("TB_", winsize[w])]]
        df2<-df2[,c("chrom","start","end","cov23")]
        df2<-df2[order(df2$cov23, decreasing=T),]
        df2$top1<-"N"
        df2$top1[1:n]<-"TB"
    
        #ss
        df3<-cov.list[[paste0("SS_", winsize[w])]]
        df3<-df3[,c("chrom","start","end","cov23")]
        df3<-df3[order(df3$cov23, decreasing=T),]
        df3$top1<-"N"
        df3$top1[1:n]<-"SS"

        co<-rbind(df1,df2,df3)

        co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
        co$top1<-factor(co$top1, levels=c("PWS","TB","SS","N"))
        ymax<-max(co$cov23, na.rm=T)
        ggplot(co, aes(x=start/1000000, y=cov23, color=top1))+
            geom_point(size=0.5)+
            facet_wrap(~chrom, ncol=4)+
            theme_classic()+ylim(-0.1,ymax)+
            ylab("Covariance")+xlab('Postion (Mb)')+
            ggtitle(paste0(winsize[w]," window ",cv[i]))+
            scale_x_continuous(labels = comma)+
            #scale_color_discrete(breaks=c("PWS","SS","TB"))+
            scale_color_manual(values=c("deeppink","orange",gre,"#ADD8E666"), labels=c("PWS","TB","SS", ""))+
            guides(color = guide_legend(override.aes = list(color=c("deeppink","orange",gre,         "white")),title=element_text("Top 1% outliers"))) 
        ggsave(paste0("../Output/COV/COVscan_3pop/3Pops.cov23_3Pops_perChrom_",winsize[w], "Window_Outliers.png"), width = 10, height = 9, dpi=300)
        }
    }
}

2.2.1 Whole genome plots without facet wrap

cv<-c("cov12","cov13","cov23")
for (i in 1:length(cv)){
    if (i==1|i==2){
        df1<-cov.list[["PWS_100k"]]
        df1<-df1[order(df1[,cv[i]], decreasing=T),]
        n<-ceiling(nrow(df1)*0.01) #top1% region
        df1$top1<-"N"
        df1$top1[1:n]<-"PWS"
        
        df2<-cov.list[["TB_100k"]]
        df2<-df2[order(df2[,cv[i]], decreasing=T),]
        df2$top1<-"N"
        df2$top1[1:n]<-"TB"

        co<-rbind(df1, df2)
        co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
        colnames(co)[which(colnames(co)==cv[i])]<-"cov"
        #assgin colors
        co$top1<-apply(co, 1, function(x) {ifelse (x['top1']=="N", x['color'], x['top1'])} )
        co$top1<-factor(co$top1, levels=c("PWS","TB","col1","col2"))
        
        #count the number of sites per chromosomes
        poss<-data.frame(chr=paste0("chr",1:26))
        k=1
        for (j in 1:26){
            df<-df1[df1$chr==paste0("chr",j),]
            poss$start[j]<-k
            poss$end[j]<-k+nrow(df)-1
            k=k+nrow(df)
        }
        poss$x<-poss$start+(poss$end-poss$start)/2
        ymax<-max(co$cov, na.rm=T)
        ggplot(co, aes(x=index, y=cov, color=top1))+
            geom_point(size=0.5)+
            theme_classic()+ylim(-0.1,ymax)+
            scale_color_manual(values=c("#FF3293B3","#0433FFB3" ,"#ADD8E680","#C0C0C088"), labels=c("PWS", "TB", "",""))+
                ylab("Covariance")+
                ggtitle(paste0(" 100k window ",cv[i]))+
                guides(color = guide_legend(override.aes = list(color=c("deeppink","#0433FF","white","white"), size=2), title=element_text("Outlier (1%)", size=10)))+
            scale_x_continuous(name="Chromosome", breaks=poss$x, labels=1:26)
        ggsave(paste0("../Output/COV/COVscan_3pop/3Pops.",cv[i],"_100k_Window_Outliers.png"), width = 10, height = 3.5, dpi=300)
        }}
    if (i==3){
        #pws
        df1<-cov.list[["PWS_100k"]]
        df1<-df1[,c("chrom","start","end","cov23","index","color")]
        df1<-df1[order(df1$cov23, decreasing=T),]
        n<-ceiling(nrow(df1)*0.01) #top1% region
        df1$top1<-df1$color
        df1$top1[1:n]<-"PWS"
        #tb
        df2<-cov.list[["TB_100k"]]
        df2<-df2[,c("chrom","start","end","cov23","index","color")]
        df2<-df2[order(df2$cov23, decreasing=T),]
        df2$top1<-df2$color
        df2$top1[1:n]<-"TB"
        #ss
        df3<-cov.list[["SS_100k"]]
        df3<-df3[,c("chrom","start","end","cov23","index","color")]
        df3<-df3[order(df3$cov23, decreasing=T),]
        df3$top1<-df3$color
        df3$top1[1:n]<-"SS"

        co<-rbind(df1,df2,df3)
        co$top1<-factor(co$top1, levels=c("PWS","TB","SS","col1","col2"))
        
        #count the number of sites per chromosomes
        poss<-data.frame(chr=paste0("chr",1:26))
        k=1
        for (j in 1:26){
            df<-df1[df1$chr==paste0("chr",j),]
            poss$start[j]<-k
            poss$end[j]<-k+nrow(df)-1
            k=k+nrow(df)
        }
        poss$x<-poss$start+(poss$end-poss$start)/2
        ymax<-max(co$cov, na.rm=T)
        ggplot(co, aes(x=index, y=cov23, color=top1))+
            geom_point(size=0.5)+
            theme_classic()+ylim(-0.1,ymax)+
            scale_color_manual(values=c("#FF3293B3","#0433FFB3","#008F00B3" ,"#ADD8E680","#C0C0C088"), labels=c("PWS", "TB","SS", "",""))+
                ylab("Covariance")+
                ggtitle(paste0(" 100k window ",cv[i]))+
                guides(color = guide_legend(override.aes = list(color=c("deeppink","#0433FF","#008F00","white","white"), size=2), title=element_text("Outlier (1%)")))+
            scale_x_continuous(name="Chromosome", breaks=poss$x, labels=1:26)+
            theme(legend.title = element_text(size=10))
        ggsave(paste0("../Output/COV/COVscan_3pop/3Pops.",cv[i],"_100k_Window_Outliers.png"), width = 10, height = 3.5, dpi=300)
        }
    }
}

## Plot 3 time periods together for PWS and TB
Cov<-data.frame()
for (i in 1:length(cv)){
    df1<-cov.list[["PWS_100k"]]
    df1<-df1[order(df1[,cv[i]], decreasing=T),]
    n<-ceiling(nrow(df1)*0.01) #top1% region
    df1$top1<-"N"
    df1$top1[1:n]<-"PWS"
    
    df2<-cov.list[["TB_100k"]]
    df2<-df2[order(df2[,cv[i]], decreasing=T),]
    df2$top1<-"N"
    df2$top1[1:n]<-"TB"
    co<-rbind(df1, df2)
    co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
    colnames(co)[which(colnames(co)==cv[i])]<-"cov"
    #assgin colors
    co$top1<-apply(co, 1, function(x) {ifelse (x['top1']=="N", x['color'], x['top1'])} )
    co$top1<-factor(co$top1, levels=c("PWS","TB","col1","col2"))
    co$time<-cv[i]
    
    Cov<-rbind(Cov, co[,c("index", "cov","top1","time")])
}

#count the number of sites per chromosomes
df1<-cov.list[["PWS_100k"]]
poss<-data.frame(chr=paste0("chr",1:26))
k=1
for (j in 1:26){
        df<-df1[df1$chr==paste0("chr",j),]
        poss$start[j]<-k
        poss$end[j]<-k+nrow(df)-1
        k=k+nrow(df)
}
poss$x<-poss$start+(poss$end-poss$start)/2
ymax<-max(co$cov, na.rm=T)
ggplot(Cov, aes(x=index, y=cov, color=top1))+
    facet_wrap(~time, ncol=1)+
    geom_point(size=0.5)+
    theme_classic()+ylim(-0.1,ymax)+
    scale_color_manual(values=c("#FF3293B3","#0433FFB3" ,"#ADD8E680","#C0C0C088"), labels=c("PWS", "TB", "",""))+
    ylab("Covariance")+
    guides(color = guide_legend(override.aes = list(color=c("deeppink","#0433FF","white","white"), size=2), title=element_text("Outlier (1%)", size=10)))+
    scale_x_continuous(name="Chromosome", breaks=poss$x, labels=1:26)

ggsave(paste0("../Output/COV/COVscan_3pop/PWS_TB_100k_Window_Outliers.png"), width = 11, height = 5, dpi=300)
        }}

2.3 Overlapping outlier regions between different window sizes (top1%)

# How window size affects outlier regions

#COV12
cv<-c("cov12","cov13","cov23")
winsize<-c("50k","75k","100k")
pairs<-t(combn(winsize, 2))
pairs<-data.frame(pairs)
colnames(pairs)<-paste0("window",1:2)
Ov1<-pairs
Ov2<-pairs
Ov3<-pairs
#populations<-c("PWS","TB")
for (i in 1:length(cv)){
    df<-read.csv(paste0("../Output/COV/3pops_top1percent_outlier_regions.", cv[i], "_new.csv"))
    df$id<-paste0(df$chrom,"_",df$start)
    pws<-df[df$pop=="PWS",]
    tb<-df[df$pop=="TB",]
    if (i==3) ss<-df[df$pop=="SS",]
        
    for(j in 1:nrow(pairs)){
        isec<-intersect(pws$id[pws$window==pairs[j,1]], pws$id[pws$window==pairs[j,2]]) 
        Ov1[j, cv[i]]<-length(isec)
        isec2<-intersect(tb$id[tb$window==pairs[j,1]], tb$id[tb$window==pairs[j,2]]) 
        Ov2[j, cv[i]]<-length(isec2)
        if (i==3) {
              isec3<-intersect(ss$id[ss$window==pairs[j,1]], ss$id[ss$window==pairs[j,2]]) 
              Ov3[j, cv[i]]<-length(isec3)
         }
        #### Check chromosome region overlap +-200,000 bases
        
        for(p in 1:length(pops)){
            if (p==1) df_pop<-pws
            if (p==2) df_pop<-tb
            if (p==3 & exists("ss")) {df_pop<-ss}
                else next
        
            time1<-df_pop[df_pop$window==pairs[j,1],]
            time2<-df_pop[df_pop$window==pairs[j,2],]
             
            overlps<-data.frame()
            overlps2<-data.frame()
        
            for (n in 1: nrow(time1)){
                re<-time2[time2$chrom==time1$chrom[n],]
                if (nrow(re)>=1){
                    for (s in 1: nrow(re)){
                        if (re$start[s]<=time1$start[n]+200000 & re$start[s]>=time1$start[n]-200000){
                            overlps<-rbind(overlps, re[s,])
                            overlps2<-rbind(overlps2,time1[n,])}
                }}
            }      
            #Overlapping windows:
            ov<-data.frame(id=overlps$id)
            for (n in 1: nrow(overlps)){
                if (overlps$start[n]<overlps2$start[n]) {ov$start[n]<-overlps$start[n]; ov$end[n]<-overlps2$end[n]}
                if (overlps$start[n]>=overlps2$start[n]) {ov$start[n]<-overlps2$start[n];ov$end[n]<-overlps$end[n]}
            }
            ov[,paste0("cov.",pairs[j,1])]<-overlps[,4]
            ov[,paste0("cov.",pairs[j,2])]<-overlps2[,4]
            write.csv(ov, paste0("../Output/COV/COVscan_3pop/Overlap_regions_",pops[p],".", pairs[j,1],".",pairs[j,2], ".",cv[i], "_plusminus200k.csv"), row.names = F)
        }
    }
}
write.csv(Ov1, paste0("../Output/COV/COVscan_3pop/Overlapping_window_counts_PWS.csv"))
write.csv(Ov2, paste0("../Output/COV/COVscan_3pop/Overlapping_window_counts_TB.csv"))
write.csv(Ov3, paste0("../Output/COV/COVscan_3pop/Overlapping_window_counts_SS.csv"))

2.4 Overlapping outlier regions between different populations

#100k
cv<-c("cov12","cov13","cov23")
pairs<-t(combn(pops, 2))
pairs<-data.frame(pairs)
colnames(pairs)<-paste0("pop",1:2)
Ov_direct<-data.frame(cov=c(cv[1:2],"cov23-PT","cov23-PS","cov23-ST" ,"cov23-3"))
Ov_300<-data.frame(cov=c(cv[1:2],"cov23-PT","cov23-PS","cov23-ST" ,"cov23-3"))
for (i in 1:length(cv)){
    df<-read.csv(paste0("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.", cv[i], "_new.csv"))
    df<-df[df$window=="100k",]
    df$id<-paste0(df$chrom,"_",df$start)
    
    if (i!=3){
        #exact overlaps
        isec<-intersect(df$id[df$pop=="PWS"], df$id[df$pop=="TB"]) 
        Ov_direct$count[i]<-length(isec)
        
        #### Check chromosome region overlap +-200,000 bases
        pop1<-df[df$pop=="PWS",]
        pop2<-df[df$pop=="TB",]
        overlps<-data.frame()
        overlps2<-data.frame()
        for (n in 1: nrow(pop1)){
            re<-pop2[pop2$chrom==pop1$chrom[n],]
            if (nrow(re)>=1){
                for (s in 1: nrow(re)){
                    if (re$start[s]<=pop1$start[n]+300000 & re$start[s]>=pop1$start[n]-300000){
                        overlps<-rbind(overlps, re[s,])
                        overlps2<-rbind(overlps2,pop1[n,])}
                }
            }
        }
        # Merge two tables into one summary overlap table:
        ov<-data.frame(id=overlps$id)
        for (n in 1: nrow(overlps)){
            if (overlps$start[n]<overlps2$start[n]) {ov$start[n]<-overlps$start[n]; ov$end[n]<-overlps2$end[n]}
            if (overlps$start[n]>=overlps2$start[n]) {ov$start[n]<-overlps2$start[n];ov$end[n]<-overlps$end[n]}
        }
        ov[,"cov.PWS"]<-overlps[,4]
        ov[,"cov.TB"]<-overlps2[,4]
        write.csv(ov, paste0("../Output/COV/COVscan_3pop/Overlap_regions_",cv[i],"_plusminus300k.csv"), row.names = F)
        Ov_300$count[i]<-nrow(ov)
        }
        
    if (i==3){
        isec<-intersect(df$id[df$pop=="PWS"], df$id[df$pop=="TB"]) 
        isec2<-intersect(df$id[df$pop=="PWS"], df$id[df$pop=="SS"]) 
        isec3<-intersect(df$id[df$pop=="SS"], df$id[df$pop=="TB"]) 
        Ov_direct$count[i]<-length(isec)
        Ov_direct$count[i+1]<-length(isec2)
        Ov_direct$count[i+2]<-length(isec)
        Ov_direct$count[i+3]<-length(intersect(df$id[df$pop=="SS"], intersect(df$id[df$pop=="PWS"], df$id[df$pop=="TB"])))
        
        for(j in 1:nrow(pairs)){
        #### Check chromosome region overlap +-200,000 bases
            pop1<-df[df$pop==pairs[j,1],]
            pop2<-df[df$pop==pairs[j,2],]
            overlps<-data.frame()
            overlps2<-data.frame()
            for (n in 1: nrow(pop1)){
                re<-pop2[pop2$chrom==pop1$chrom[n],]
                if (nrow(re)>=1){
                    for (s in 1: nrow(re)){
                        if (re$start[s]<=pop1$start[n]+300000 & re$start[s]>=pop1$start[n]-300000){
                            overlps<-rbind(overlps, re[s,])
                            overlps2<-rbind(overlps2,pop1[n,])}
                    }
                }
            }
        # Merge two tables into one summary overlap table:
            ov<-data.frame(id=overlps$id)
            for (n in 1: nrow(overlps)){
                if (overlps$start[n]<overlps2$start[n]) {ov$start[n]<-overlps$start[n]; ov$end[n]<-overlps2$end[n]}
                if (overlps$start[n]>=overlps2$start[n]) {ov$start[n]<-overlps2$start[n];ov$end[n]<-overlps$end[n]}
            }
        
            ov[,paste0("cov.",pairs[j,1])]<-overlps[,4]
            ov[,paste0("cov.",pairs[j,2])]<-overlps2[,4]
            ov<-ov[!duplicated(ov),]
            write.csv(ov, paste0("../Output/COV/COVscan_3pop/Overlap_regions_",cv[i],"_",pairs[j,1],".", pairs[j,2],"_plusminus300k.csv"), row.names = F)
            Ov_300$count[i+j-1]<-nrow(ov)
    }
    }
}
write.csv(Ov_direct, paste0("../Output/COV/COVscan_3pop/DDirect_Overlapping_regions_counts_3pop_summary.csv"))
Ov_300$count[6]<-NA
write.csv(Ov_300, paste0("../Output/COV/COVscan_3pop/Overlapping_regions_counts_3pop_plusMinus300k.csv"))

3 Run the snpEff pipeline to find annotation in the outlier regions (100k-window+-100k)

3.1 Create a script to run SnpEff

Create VCF files with selected regions & run snpEff

#Create bed files
cv<-c("cov12","cov13","cov23")
#Prevent scientific notation in bed files
options(scipen=999)

#The first line of bed files is often not red by vcftools
for (i in 1:3){
    df<-read.csv(paste0("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.",cv[i],"_new.csv"))
    #select 100k
    df<-df[df$window=="100k",]
    #add 100k
    df$start<-df$start-100000
    df$end<-df$end+100000
    dfp<-df[df$pop=="PWS",1:3]
    colnames(dfp)<-c('track type=bedGraph', '1','1')
    write.table(dfp, paste0("../Output/COV/COVscan_3pop/PWS_outliers_",cv[i],"_new.bed"),quote = F, row.names = F, col.names = T,sep = "\t")
    dft<-df[df$pop=="TB",1:3]
    colnames(dft)<-c('track type=bedGraph', '1','1')
    write.table(dft, paste0("../Output/COV/COVscan_3pop/TB_outliers_",cv[i],"_new.bed"),quote = F, row.names = F, col.names = F,sep = "\t")
    
    if (i==3){
        dfs<-df[df$pop=="SS",1:3]
        colnames(dfs)<-c('track type=bedGraph', '1','1')
        write.table(dfs, paste0("../Output/COV/COVscan_3pop/SS_outliers_",cv[i],"_new.bed"),quote = F, row.names = F, col.names = F,sep = "\t")
    }
}

#for 50k and 75k in PWS
for (i in 1:3){
    df<-read.csv(paste0("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.",cv[i],"_new.csv"))
    df<-df[df$pop=="PWS",]
    df$start<-df$start-100000
    df$end<-df$end+100000
    df50<-df[df$window=="50k",1:3]
    df75<-df[df$window=="75k",1:3]
    colnames(df50)<-c('track type=bedGraph', '1','1')
    colnames(df75)<-c('track type=bedGraph', '1','1')
    
    write.table(df50, paste0("../Output/COV/COVscan_3pop/PWS_outliers_",cv[i],"_50k.bed"),quote = F, row.names = F, col.names = T,sep = "\t")
    write.table(df75, paste0("../Output/COV/COVscan_3pop/PWS_outliers_",cv[i],"_75k.bed"),quote = F, row.names = F, col.names = T,sep = "\t")
}

# Create a bash script to run snpEff
bedfiles<-list.files("../Output/COV/COVscan_3pop/", pattern="*_new.bed")

sink("../COVscan_createVCFs_3Pops2.sh")
cat("#!/bin/bash \n\n")
for (i in 1:length(bedfiles)){
    fname<-gsub(".bed",'', bedfiles[i])
    cat(paste0("vcftools --gzvcf Data/new_vcf/3pop/3pops.MD7000_NS0.5_maf05.vcf.gz --bed Output/COV/COVscan_3pop/", bedfiles[i], " --out Output/COV/COVscan_3pop/", fname," --recode --keep-INFO-all \n"))
}
sink(NULL)  

#create a bash script to run snpEff
vfiles<-list.files("../Output/COV/COVscan_3pop/", pattern=".recode.vcf")

sink("~/programs/snpEff/runsnpEff_cov_3pop.sh")
cat("#!/bin/bash \n\n")
for (i in 1:length(vfiles)){
    fname<-gsub("_new.recode.vcf","",vfiles[i])
    cat(paste0("java -Xmx8g -jar snpEff.jar Ch_v2.0.2.99 ~/Projects/PacHerring/Output/COV/COVscan_3pop/",vfiles[i], " -stats ~/Projects/PacHerring/Output/COV/COVscan_3pop/",fname,".html >  ~/Projects/PacHerring/Output/COV/COVscan_3pop/Anno.",fname,".vcf \n"))
    
    #extract the annotation information
    cat(paste0("bcftools query -f '%CHROM %POS %INFO/AF %INFO/ANN\\n' ~/Projects/PacHerring/Output/COV/COVscan_3pop/Anno.",fname,".vcf > ~/Projects/PacHerring/Output/COV/COVscan_3pop/",fname,"_annotation \n\n"))

}
sink(NULL)  

3.2 Create summary gene files from snpEff and check overlapping genes.

## Create summary files of snpEff results (gene annotations in the regions of interest) and reformat as a ShinyGo input 

#create gene list for ShinyGo
gfiles<-list.files("../Output/COV/COVscan_3pop/", pattern="genes.txt")

for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan_3pop/",gfiles[i]), sep="\t")
    df<-df[,1:7]
    colnames(df)<-c("GeneName","GeneId","TranscriptId","BioType","variants_impact_HIGH","variants_impact_LOW",  "variants_impact_MODERATE")
    
    fname<-gsub(".genes.txt","",gfiles[i])
    genes<-unique(df$GeneId)
    sink(paste0("../Output/COV/COVscan_3pop/geneIDlist_",fname,".txt"))
    cat(paste0(genes,"; "))
    sink(NULL)
}

#Annotation infor from SnpEff
for (c in 1:3){
    if (c!=3){
    for (p in 1:2){
        ano<-read.table(paste0("../Output/COV/COVscan_3pop/",pops[p],"_outliers_",cv[c],"_annotation"), header = F)
        annotations<-data.frame()
        for (i in 1: nrow(ano)){
            anns<-unlist(strsplit(ano$V4[i], "\\,|\\|"))
            annm<-data.frame(matrix(anns,ncol = 16, byrow = TRUE))
            annm<-annm[,c(2,3,4,5,8)]
            colnames(annm)<-c("Effect","Putative_impact","Gene_name","Gene_ID","Feature type")
            annm<-annm[!duplicated(annm), ]
            annm$chr<-ano$V1[i]
            annm$chr<-ano$V1[i]
            annm$pos<-ano$V2[i]
            annm$AF<- ano$V3[i]
            annotations<-rbind(annotations, annm)
        }     
        annotations<-annotations[,c(6:8,1:5)]
        annotations<-annotations[!duplicated(annotations[,1:2]),]
        write.csv(annotations, paste0("../Output/COV/COVscan_3pop/Genes_",pops[p],"_outliers_100k_",cv[c],".csv"), row.names = F)
    }
    }
    if (c==3){
        for (p in 1:3){
        ano<-read.table(paste0("../Output/COV/COVscan_3pop/",pops[p],"_outliers_",cv[c],"_annotation"), header = F)
        annotations<-data.frame()
        for (i in 1: nrow(ano)){
            anns<-unlist(strsplit(ano$V4[i], "\\,|\\|"))
            annm<-data.frame(matrix(anns,ncol = 16, byrow = TRUE))
            annm<-annm[,c(2,3,4,5,8)]
            colnames(annm)<-c("Effect","Putative_impact","Gene_name","Gene_ID","Feature type")
            annm<-annm[!duplicated(annm), ]
            annm$chr<-ano$V1[i]
            annm$chr<-ano$V1[i]
            annm$pos<-ano$V2[i]
            annm$AF<- ano$V3[i]
            annotations<-rbind(annotations, annm)
        }     
        annotations<-annotations[,c(6:8,1:5)]
        annotations<-annotations[!duplicated(annotations[,1:2]),]
        write.csv(annotations, paste0("../Output/COV/COVscan_3pop/Genes_",pops[p],"_outliers_100k_",cv[c],".csv"), row.names = F)
    }
}
  
}

3.3 Find the intersecting gene names across time points within a population

# Find the intersecting gene names across time points
gfiles2<-list.files("../Output/COV/COVscan_3pop/", pattern="geneIDlist")
glist<-list()
for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan_3pop/",gfiles2[i]), sep=";")
    df<-t(df)
    df<-gsub(" ","",df)
    df2<-df[!is.na(df)]
    vname<-gsub(".txt","",gfiles2[i],)
    vname<-gsub("geneIDlist_","", vname)
    glist[[i]]<-df2
    names(glist)[i]<-vname
}

times<-c("cov12","cov13","cov23")
common<-list()
common_genes<-data.frame(time=times)
for (i in 1:3){
    tlist<-glist[grep(times[i], names(glist))]
    if (i !=3){
        common[[i]]<-intersect(tlist[[1]], tlist[[2]])
        names(common)[[i]]<-times[i]
        common_genes$PWS[i]<-length(tlist[[grep("PWS", names(tlist))]])
        common_genes$TB[i]<-length(tlist[[grep("TB", names(tlist))]])
        common_genes$SS[i]<-NA
        common_genes$common_PWS.TB[i]<-length(intersect(tlist[[1]], tlist[[2]]))
    }
    if (i==3){
        common_genes$PWS[i]<-length(tlist[[grep("PWS", names(tlist))]])
        common_genes$TB[i]<-length(tlist[[grep("TB", names(tlist))]])
        common_genes$SS[i]<-length(tlist[[grep("SS", names(tlist))]])
        common_genes$common_PWS.TB[i]<-length(intersect(tlist[[1]], tlist[[3]]))
        common_genes$common_PWS.SS[i]<-length(intersect(tlist[[1]], tlist[[2]]))
        common_genes$common_SS.TB[i]<-length(intersect(tlist[[2]], tlist[[3]]))
        common_genes$common3[i]<-length(intersect(tlist[[1]],intersect(tlist[[2]], tlist[[3]])))
        k=i
        common[[k]]<-intersect(tlist[[1]], tlist[[2]])
        names(common)[[k]]<-paste0(times[i],"_PWS.SS")
        k=k+1
        common[[k]]<-intersect(tlist[[1]], tlist[[3]])
        names(common)[[k]]<-paste0(times[i],"_PWS.TB")
        k=k+1
        common[[k]]<-intersect(tlist[[2]], tlist[[3]])
        names(common)[[k]]<-paste0(times[i],"_SS.TB")
        k=k+1
        common[[k]]<-intersect(tlist[[1]],intersect(tlist[[2]], tlist[[3]]))
        names(common)[[k]]<-paste0(times[i],"_3pops")
        }
}
    
write.csv(common_genes, "../Output/COV/COVscan_3pop/Common_genes_3pops.csv")


#What are the overlapping gene names
#aggregate all gene names
Genes<-data.frame()
for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan_3pop/",gfiles[i]), sep="\t")
    df<-df[,1:2]
    colnames(df)<-c("GeneName","GeneId")
    df<-df[!duplicated(df),]
    Genes<-rbind(Genes, df)
    Genes<-Genes[!duplicated(Genes),]
}

for (i in 1: length(common)){
    gids<-common[[i]]
    df<-data.frame(GeneId=gids)
    
    df<-merge(df, Genes, by="GeneId")
    write.csv(df, paste0("../Output/COV/COVscan_3pop/Common_genes_", names(common)[i],".csv"), row.names = F)
}

3.3.0.1 Overlapping gene numbers

## Check chromosome overlap

4 Compare results from PH_MD7000 (all pops together) and 3Pops-NS0.5 (filter for 3 pops) VCF files

pws1<-read.csv("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.cov12_new.csv")
pws1<-pws1[pws1$pop=="PWS" & pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov12.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"3Pops"
pws2$vcf<-"PH"
pws<-rbind(pws1[,c("chrom","start","end","cov12","vcf")],pws2[,c("chrom","start","end","cov12","vcf")])
pws$chrom<-factor(pws$chrom, levels=paste0("chr", 1:26))
ggplot(data=pws,aes(x=start/1000000, y=cov12, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point(position=position_dodge(width = 0.7), alpha=0.5)+xlab("Position (Mb)")+
    ggtitle("PWS COV12")
ggsave("../Output/COV/COVscan_3pop/PWS_PH.vs.3Pops_outlier_overlap_cov12.png", width = 10, height = 8, dpi=300)

pws1<-read.csv("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.cov23_new.csv")
pws1<-pws1[pws1$pop=="PWS" & pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov23.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"PH"
pws2$vcf<-"3Pops"
pws<-rbind(pws1[,c("chrom","start","end","cov23","vcf")],pws2[,c("chrom","start","end","cov23","vcf")])
pws$chrom<-factor(pws$chrom, levels=paste0("chr", 1:26))
ggplot(data=pws,aes(x=start/1000000, y=cov23, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point(position=position_dodge(width = 0.7), alpha=0.5)+xlab("Position (Mb)")+
    ggtitle("PWS COV23")
ggsave("../Output/COV/COVscan_3pop/PWS_PH.vs.3Pops_outlier_overlap_cov23.png", width = 10, height = 8, dpi=300)

pws1<-read.csv("../Output/COV/COVscan_3pop/3pops_top1percent_outlier_regions.cov13_new.csv")
pws1<-pws1[pws1$pop=="PWS" & pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov13.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"PH"
pws2$vcf<-"3Pops"
pws<-rbind(pws1[,c("chrom","start","end","cov13","vcf")],pws2[,c("chrom","start","end","cov13","vcf")])
pws$chrom<-factor(pws$chrom, levels=paste0("chr", 1:26))
ggplot(data=pws,aes(x=start/1000000, y=cov13, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point(position=position_dodge(width = 0.7), alpha=0.5)+xlab("Position (Mb)")+
    ggtitle("PWS COV13")
ggsave("../Output/COV/COVscan_3pop/PWS_PH.vs.3Pops_outlier_overlap_cov13.png", width = 10, height = 8, dpi=300)

5 Interpopulation comparison per time period

### Interpopulation comparisons
#decode the samples to create the right matrix
cv<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_3pops.csv", header = F)
labs<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_labels_3pops.csv" )
labs<-labs[,-1]
cvm<-data.frame(label=as.vector(t(labs)), cov=as.vector(t(cv)))

#rearrange based on comparions: covariance between populations within the same period
#PopYr Symbols
# PH 1 'PWS', 1991
# PH 2 'PWS', 1996
# PH 3 'PWS', 2006
# PH 4 'PWS', 2017
# PH 5 'SS',  1991
# PH 6 'SS',  1996
# PH 7 'SS',  2006
# PH 8 'SS',  2017
# PH 9 'TB',  1991
# PH 10'TB',  1996
# PH 11'TB',  2006
# PH 12'TB',  2017

Covs<-data.frame(pops=rep(c("PWS.vs.SS", "PWS.vs.TB",  "SS.vs.TB"), times=6),
                 period=c(rep("1991-1996", times=3),rep("1996-2006", times=3), rep("2006-2017", times=3)))

Covs$cov<-c(NA, cvm$cov[cvm$label=="cov(PH: 2-1, PH: 10-9)"],NA,
            cvm$cov[cvm$label=="cov(PH: 3-2, PH: 7-6)"],cvm$cov[cvm$label=="cov(PH: 3-2, PH: 11-10)"], 
            cvm$cov[cvm$label=="cov(PH: 7-6, PH: 11-10)"],
            cvm$cov[cvm$label=="cov(PH: 4-3, PH: 8-7)"],cvm$cov[cvm$label=="cov(PH: 4-3, PH: 12-11)"],cvm$cov[cvm$label=="cov(PH: 8-7, PH: 12-11)"])


#C.I.
cis<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_COV_Interpop_comparison_CIs.csv")
cis<-cis[,-1]
cim<-data.frame(label=as.vector(t(labs)), ci_l=as.vector(t(cis[1:11,])))
cim$ci_h<-as.vector(t(cis[12:22,]))

Covs$ci_l<-as.numeric(c(NA,cim$ci_l[cim$label=="cov(PH: 2-1, PH: 10-9)"],NA,
                      cim$ci_l[cim$label=="cov(PH: 3-2, PH: 7-6)"],cim$ci_l[cim$label=="cov(PH: 3-2, PH: 11-10)"], cim$ci_l[cim$label=="cov(PH: 7-6, PH: 11-10)"],
                      cim$ci_l[cim$label=="cov(PH: 4-3, PH: 8-7)"],cim$ci_l[cim$label=="cov(PH: 4-3, PH: 12-11)"], cim$ci_l[cim$label=="cov(PH: 8-7, PH: 12-11)"]))

Covs$ci_h<-as.numeric(c(NA, cim$ci_h[cim$label=="cov(PH: 2-1, PH: 10-9)"],NA,
                      cim$ci_h[cim$label=="cov(PH: 3-2, PH: 7-6)"],cim$ci_h[cim$label=="cov(PH: 3-2, PH: 11-10)"], cim$ci_h[cim$label=="cov(PH: 7-6, PH: 11-10)"],
                      cim$ci_h[cim$label=="cov(PH: 4-3, PH: 8-7)"],cim$ci_h[cim$label=="cov(PH: 4-3, PH: 12-11)"], cim$ci_h[cim$label=="cov(PH: 8-7, PH: 12-11)"]))

#Barplot
ggplot(Covs, aes(x=period, y=cov, fill=pops))+
    geom_bar(stat="identity",position=position_dodge(width = 0.7), width=0.8)+
    ylab("Covariance")+xlab('')+theme_classic()+
    geom_hline(yintercept = 0,color="gray70", size=0.3)+
    scale_fill_manual(values=cols[c(2,1,3)])+
    theme(legend.title = element_blank())+
    scale_y_continuous(labels = comma)+
    ylim(-0.0013, 0.002)+
    geom_errorbar(aes(ymin=ci_l, ymax=ci_h), width=.2, size=.2, position=position_dodge(width = 0.7))
ggsave("../Output/COV/Interpop_cov_comparison_3Pops_new.png",width = 4.7, height = 3, dpi=300)

#Point plot
ggplot(Covs, aes(x=period, y=cov, color=pops))+
    geom_point(position=position_dodge(width = 0.7), size=4)+
    ylab("Covariance")+xlab('')+theme_classic()+
    geom_hline(yintercept = 0,color="gray70", size=0.3)+
    scale_color_manual(values=cols[c(2,1,3)])+
    theme(legend.title = element_blank())+
    scale_y_continuous(labels = comma)+
    geom_errorbar(aes(ymin=ci_l, ymax=ci_h), width=.2, size=.2, position=position_dodge(width = 0.7))+
    ylim(-0.0013, 0.002)+
     geom_vline(xintercept = c(1.5,2.5), color="gray", size=0.3)
ggsave("../Output/COV/Interpop_cov_comparison_3Pops_new_PointPlot.png",width = 4.7, height = 3, dpi=300)

#line plot
Covs$time<-1
Covs$time[Covs$period=="1996-2006"]<-2
Covs$time[Covs$period=="2006-2017"]<-3
Covs<-Covs[order(Covs$time),]
ggplot(Covs, aes(x=time, y=cov, color=pops, group=pops))+
    geom_point(position=position_dodge(width = 0.7), size=4)+
    geom_path(position=position_dodge(width = 0.7))+
    ylab("Covariance")+xlab('')+theme_classic()+
    geom_hline(yintercept = 0,color="gray70", size=0.3)+
    scale_color_manual(values=cols[c(2,1,3)])+
    theme(legend.title = element_blank())+
    scale_y_continuous(labels = comma)+
    geom_errorbar(aes(ymin=ci_l, ymax=ci_h), width=.2, size=.2, position=position_dodge(width = 0.7))+
    ylim(-0.0013, 0.002)+
     geom_vline(xintercept = c(1.5,2.5), color="gray", size=0.3)+
    scale_x_continuous(breaks=c(1,2,3), labels = c("1991-1996","1996-2006","2006-2017"))
ggsave("../Output/COV/Interpop_cov_comparison_3Pops_new_LinePlot.png",width = 4.7, height = 3, dpi=300)

5.1 Longer time period

## Longer time-period
Covs2<-data.frame(pops=rep(c("PWS.vs.SS", "PWS.vs.TB",  "SS.vs.TB"), times=3),
                 period=c(rep("1991-2006", times=3),rep("1991-2017", times=3),rep("1996-2017", times=3)))

cv1<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_1991-2006.csv", header = F)
labs1<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_labels_1991-2006.csv" )
labs1<-labs1[,-1]
cvm1<-data.frame(label=as.vector(t(labs1)), cov=as.vector(t(cv1)))

cv2<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_1991-2017.csv", header = F)
labs2<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_labels_1991-2017.csv" )
labs2<-labs2[,-1]
cvm2<-data.frame(label=as.vector(t(labs2)), cov=as.vector(t(cv2)))

cv3<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_1996-2017.csv", header = F)
labs3<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_Covs_interPopulations_100k_labels_1996-2017.csv" )
labs3<-labs3[,-1]
cvm3<-data.frame(label=as.vector(t(labs3)), cov=as.vector(t(cv3)))

Covs2$cov<-c(NA, cvm1$cov[cvm1$label=="cov(PH: 2-1, PH: 4-3)"], NA,
             NA, cvm2$cov[cvm2$label=="cov(PH: 2-1, PH: 4-3)"], NA,
             cvm3$cov[cvm3$label=="cov(PH: 2-1, PH: 4-3)"], cvm3$cov[cvm3$label=="cov(PH: 2-1, PH: 6-5)"], cvm3$cov[cvm3$label=="cov(PH: 4-3, PH: 6-5)"])

#C.I.
cis1<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_COV_Interpop_comparison_CIs_1991-2006.csv")
cis1<-cis1[,-1]
cis2<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_COV_Interpop_comparison_CIs_1991-2017.csv")
cis2<-cis2[,-1]
cis3<-read.csv("~/Projects/Pacherring_Vincent/MD7000/GW_COV_Interpop_comparison_CIs_1996-2017.csv")
cis3<-cis3[,-1]

#cim<-data.frame(label=as.vector(t(labs)), ci_l=as.vector(t(cis1[1:4,])))
#cim$ci_h<-as.vector(t(cis[12:22,]))

Covs2$ci_l<-as.numeric(c(NA,cis1[1,3],NA,
                        NA,cis2[1,3],NA,
                      cis3[1,3],cis3[1,5],cis3[3,5]))

Covs2$ci_h<-as.numeric(c(NA,cis1[4,3],NA,
                        NA,cis2[4,3],NA,
                      cis3[6,3],cis3[6,5],cis3[8,5]))


ggplot(Covs2, aes(x=period, y=cov, fill=pops))+
    geom_bar(stat="identity",position=position_dodge(width = 0.7), width=0.8)+
    ylab("Covariance")+xlab('')+theme_classic()+
    geom_hline(yintercept = 0,color="gray70", size=0.3)+
    scale_fill_manual(values=cols[c(2,1,3)])+
    theme(legend.title = element_blank())+scale_y_continuous(labels = comma)+
    ylim(-0.0013, 0.002)+
    geom_errorbar(aes(ymin=ci_l, ymax=ci_h), width=.2, size=.2, position=position_dodge(width = 0.7))
ggsave("../Output/COV/Interpop_cov_comparison_3Pops_LonogerPeriod.png",width = 4.7, height = 3, dpi=300)

ggplot(Covs2, aes(x=period, y=cov, color=pops))+
    geom_point(position=position_dodge(width = 0.7), size=4)+
    ylab("Covariance")+xlab('')+theme_classic()+
    geom_hline(yintercept = 0,color="gray70", size=0.3)+
    scale_color_manual(values=cols[c(2,1,3)])+
    theme(legend.title = element_blank())+
    scale_y_continuous(labels = comma)+
     ylim(-0.0013, 0.002)+
    geom_errorbar(aes(ymin=ci_l, ymax=ci_h), width=.2, size=.2, position=position_dodge(width = 0.7))+
    geom_vline(xintercept = c(1.5,2.5), color="gray", size=0.3)
ggsave("../Output/COV/Interpop_cov_comparison_3PopsLonogerPeriod_PointPlot.png",width = 4.7, height = 3, dpi=300)

6 Focused freq analysis

6.1 rel gene (chr13: 23070000 - 23080000)

pops<-c("PWS91","PWS96","PWS07","PWS17")
yr<-c(1991,1996,2007,2017)
maf<-data.frame()
for (i in 1:4){
    af<-read.table(paste0("../Data/new_vcf/AF/",pops[i],".mafs"),sep="\t", header = T)
    af<-af[af$chromo=="chr13"&af$position>=23070000&af$position<=23080000,]
    af$year<-yr[i]
    maf<-rbind(maf,af)
}
#write.csv(maf,"../Output/COV/COVscan_3pop/AF_maf_chr13_23Mb.csv")

ggplot(maf, aes(x=year, y=knownEM, color=factor(position)))+
    geom_point()+
    geom_path()+ggtitle("MAF (ANGSD)")+
    ylab("maf")+
    theme(legend.title=element_blank())
ggsave("../Output/COV/COVscan/AF_ch13_23.07-23.08_angsd.png", width = 5, height=3, dpi=300)


AF<-data.frame()
for (i in 1:4){
    af<-read.table(paste0("../Data/new_vcf/AF/",pops[i],"_maf05_af.frq"),header = FALSE, skip=1, col.names = c("chr","pos","n_allele","n_sample","MajorAF","MAF"))
    af<-af[af$chr=="chr13"&af$pos>=23070000&af$pos<=23080000,]
    af$year<-yr[i]
    af$maf<-substr(af$MAF, 3,10)
    af$maf<-as.numeric(af$maf)
    af<-af[,c("chr","pos","maf","year")]
    AF<-rbind(AF,af)
}
ggplot(AF, aes(x=year, y=maf, color=factor(pos)))+
    geom_point()+
    geom_path()+ggtitle("MAF (vcftools)")+
    theme(legend.title=element_blank())
ggsave("../Output/COV/COVscan/AF_ch13_23.07-23.08.png", width = 5, height=3, dpi=300)


###TB
pops<-c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17")
yr<-c(1991,1996,2006,2017,1991,1996,2007,2017,1996,2006,2017)
maf<-data.frame()
for (i in 1:length(pops)){
    af<-read.table(paste0("../Data/new_vcf/AF/",pops[i],".mafs"),sep="\t", header = T)
    af<-af[af$chromo=="chr13"&af$position>=23070000&af$position<=23080000,]
    af$year<-yr[i]
    af$pop<-sub("\\d\\d","", pops[i])
    maf<-rbind(maf,af)
}
#write.csv(maf,"../Output/COV/COVscan_3pop/AF_maf_chr13_23Mb.csv")
ggplot(maf, aes(x=year, y=knownEM, color=pop))+
    facet_wrap(~factor(position))+
    geom_point()+
    geom_path()+ggtitle("Chr13 (rel gene)")+
    ylab("maf")+
    theme(legend.title=element_blank())
ggsave("../Output/COV/COVscan/AF_ch13_23.07-23.08_angsd.png", width = 5, height=3, dpi=300)